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Label-efficient audio classification through multitask learning and self-supervision
We demonstrate that, in scenarios with limited labeled training data, one can significantly improve the performance of three different supervised classification tasks individually by up to 6% through simultaneous training with these additional self-supervised tasks.
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks
This necessitates increased memory footprint and computational requirements for training.
Patch-CNN: Training data-efficient deep learning for high-fidelity diffusion tensor estimation from minimal diffusion protocols
To overcome these limitations, we propose Patch-CNN, a neural network with a minimal (non-voxel-wise) convolutional kernel (3$\times$3$\times$3).
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